A Survey of the Four Pillars for Small Object Detection: Multiscale Representation, Contextual Information, Super-Resolution, and Region Proposal

By Guang Chen, Haitao Wang, Kai Chen, Zhijun Li, Zida Song, Yinlong Liu, Wenkai Chen, Alois C. Knoll. IEEE Transactions on Systems, Man & Cybernetics. Systems, 2022

306 citations recorded by the source corpus.

Although great progress has been made in generic object detection by advanced deep learning techniques, detecting small objects from images is still a difficult and challenging problem in the field of computer vision due to the limited size, less appearance, and geometry cues, and the lack of large-scale datasets of small targets. Improving the performance of small object detection has a wider significance in many real-world applications, such as self-driving cars, unmanned aerial vehicles, and robotics. In this article, the first-ever survey of recent studies in deep learning-based small object detection is presented. Our review begins with a brief introduction of the four pillars for small object detection, including multiscale representation, contextual information, super-resolution, and region-proposal. Then, the collection of state-of-the-art datasets for small object detection is listed. The performance of different methods on these datasets is reported later. Moreover, the state-of-the-art small object detection networks are investigated along with a special focus on the differences and modifications to improve the detection performance comparing to generic object detection architectures. Finally, several promising directions and tasks for future work in small object detection are provided. Researchers can track up-to-date studies on this webpage available at: https://github.com/tjtum-chenlab/SmallObjectDetectionList.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7cdb0bfaf9bfff9e9ef57e809020961f6e9bada7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Junwei Han
2. Gong Cheng
3. Ke Yan
4. Zhenwei Shi
5. Yair Weiss
6. Aharon Azulay
7. Guisong Xia
8. Marco Körner

In Computer Vision and Pattern Recognition

1. Gong Cheng
2. Junwei Han
3. Xiwen Yao
4. Ke Yan
5. Xiang Yuan
6. Qinghua Zeng
7. Peicheng Zhou
8. Lei Guo

Citations in the corpus

• Towards Large-Scale Small Object Detection: Survey and Benchmarks (2022, background): “Furthermore, there already exist several reviews paying their attention to small object detection [25], [26], [27], however, they either fail to the comprehensiveness and in-depth analysis because only partial reviews on limited areas were conducted, or categorize considerable algorithms belonging…”